Sand fraction prediction from seismic attributes using optimized support vector regression in an oil reservoir

Abstract

In this study, a new strategy based on integrating geostatistical seismic inversion and optimized support vector regression (OSVR) will be utilized to transform multi seismic attributes to sand fraction log. In first step, owing to compatibility relation between acoustic impedance (AI) and sand fraction, a high resolution value of this important attribute was extracted through a geostatistical seismic inversion (GSI). In second step, in addition to AI, several physical attributes are obtained from seismic data and then all of extracted attributes (AI and other seismic attributes) evaluated by step-wise regression for selecting best attributes that have highest effect on predicting sand fraction. In final step, selected attributes have been fed in the bat inspired optimized support vector regression as input and the sand fraction log is estimated. For the assessment of proposed strategy, the values of predicted sand fraction are compared with their real corresponding values in a blind well. It will be evident from the results that the proposed strategy is qualified for modeling the sand fraction as a function of seismic attributes.